Abstract: Monitoring is one of the most important steps in advanced control of complex dynamic systems. Precise information about systems behaviour, including faults indicating, enables for efficient control. The paper describes an approach to detection and localisation of pipe leakage in Drinking Water Distribution Systems (DWDS) representing complex and distributed dynamic system of large scale. Proposed MultiRegional Principal Component Analysis (MR-PCA) skilfully takes full advantage of well known PCA method and enables not only for detecting the leakages but also supports their localisation. The main idea of MR-PCA is presented on example of small water network. Next the method is applied to DWDS in Chojnice, northern Poland. DWDS Chojnice is decomposed into suitable subnetworks what makes that the monitoring process is easier and require less sensors. The subnetworks and corresponding PCA monitoring models are selected based on the network operational knowledge and information regarding its topology.

Nowadays, monitoring systems besides data gathering are able to pre-process the data, to recover and estimate not directly measured variables. However, in large scale systems there is very large quantity of information that are hard to handle and sometimes almost impossible to properly process and hence to efficiently utilised it in the control process. An example of such systems is Drinking Water Distribution Systems (DWDS) the representatives of the class of network systems. The DWDS are usually, very complex (lots of pipes, connecting nodes, pumps, tanks etc.) and distributed (in space). It entails measuring of very large number of variables necessity, in order to possess information about the system state that is necessary for efficient system control. In such situations special methods enabling for analysis of large amount of data (e.g. faults detection and isolation) are required. Advanced monitoring systems should not only visualize desired data but also be able to detect devices faults and/or the unusual system behaviour. The paper proposes an approach to detecting and localisation of water leakage in pipes by using the Principal Components Analysis (PCA) method [1]. The PCA is a method that looks for multidimensional correlation between the variables and uses it to reduce the dimensionality of problems simultaneously remaining most of original information. Mostly, large amount of real data process do not provide large amount of important information. Hence, PCA explores data to find out very meaningful ones and include them into statistical models. Moreover, these models clearly indicate the abnormal state of the system thanks to specially calculated measures (T2 and SPE). In case of DWDS such a situation might be caused by device faults (e.g. sensor or pump break down), water leakage in pipe, significant increasing of the water uptake (e.g. caused by fire brigades) etc. Detecting of fault is important however, in case of DWDS the system operator still does not know its type and localisation. Leakages detection and localisation issue is a very important and complex problem that has been widely investigated [2] – [6]. However, available active leakage control methods are basically unpractical due to costs or long leak detection and location time [4]. In the paper the novel approach the MultiRegional Principal Component Analysis (MR-PCA) method is used to detect and to locate the water leakage based on measurements from limited number of measuring devices [5]. MR-PCA tries to join operational experience of staff working in water companies and advanced mathematical analysis. Moreover, this method compromises between detection efficiency and a number of measuring devices.

The method is explained based on simple water network and followed with its application to real town case study DWDS Chojnice (northern Poland).

Zierolf, M., Polycarpou, M. and Uber, J., Development and Autocalibration of an Input-Output Model of Chlorine Transport in Drinking Water Distribution Systems, IEEE Transaction on Control Systems Technology 6, 1998.